September 2013
AUTHOR
Milan Borkovec
Managing Director
[email protected]
Ian Domowitz
Managing Director
[email protected]
Christopher Escobar
Director
[email protected]
contact
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+852.2846.3500
Canada
+1.416.874.0900
EMEA
+44.20.7670.4000
United States
+1.212.588.4000
[email protected]
www.itg.com
How Big is “Big”?
Some Evidence from Aggregate
Trading Costs in the FX Market
buyers beware
On July 3 of this year, the courts pronounced caveat emptor with respect to execution
performance in the FX market. U.S. District Judge Denise Cote threw out a lawsuit,
which accused JPMorgan Chase & Co. of breaching a fiduciary duty to custodial
clients by charging “hidden and excessive mark-ups” on currency trades. Judge
Lewis Kaplan dismissed a lawsuit directed at officials of Bank of New York Mellon, for
ignoring “red flags” or knowing that trades were being processed at the worst or
near-worst prices of the day.
In the case of JPMorgan, allegations were rejected that the custodial agreement
obligated the bank to process trades at “the best available market rate” or by any
other measure. Judge Kaplan said it was improper to hold Bank of New York Mellon
executives and directors responsible for alleged currency trading practices leading to
the lawsuit. The lawsuit did not create “reasonable doubt that the board’s inaction
was a valid exercise of business judgment.”
We have no opinion on such rulings or the legal actions which engendered them. But,
what do buyers do when caveat emptor is the way of a market? They look, or at least,
they should. An examination of currency trading activity is more involved than Judge
Cote believes, when she said, there was “nothing secret about the mark-ups”
charged, because they are disclosed in public databases and on trade confirmations.
We are still searching for those public databases, and simply note that trade
confirmations are hardly evidence of execution quality.
are we looking yet?
Credible examination of FX transaction costs is difficult, but not impossible. Early
studies by Russell Investment Group and Record Currency Management used data
from sub advisors to measure the contribution of FX to overall portfolio transaction
costs.1 In 2004, Russell found that the distribution of transactions in major
currencies was highly skewed towards the worst rates of the day, with an average
cost of nine basis points (bps). Record found costs to be between 10 and 12 bps;
they noted that “approximately one half of the audits conducted to date by Record
revealed that clients received uncompetitive FX pricing on a routine basis.” A 2010
1
Robert Collie, “It’s Time for More Choice in FX,” Russell Investment Group Viewpoint, December 2004; Record Currency Management,
“Paying Heed Pays Off,” Record Research Summary #5, July 2003; Record Currency Management, “Report to Frank Russell on Currency
Transaction Costs,” February 2005.
September 2013
2
paper from Russell shows that most FX trades of investors, who rely on the
executions of managers or custodians, are executed at prices inferior to the average
rate of the day.2
In 2007, a survey of 17 transaction cost analysis (TCA) providers revealed that none
offered foreign exchange TCA.3 Today, there are at least 9.
Some ascribe this growth to regulatory pressures. The concept of regulation is
tenuous in the global FX market, but mandates for best execution may be on the
horizon. Legal cases provide some impetus, but TCA providers are shy about
appearing on the witness stand. Customer demand, focused on process
improvement in pursuit of alpha preservation, is a driver. A survey of FX traders in
2007 suggests that 73 percent would look at reports if available, and 37 percent of
respondents would want such reports daily.4
Demand for measurement grows with changes in market structure and their
consequences. Earlier this year, ITG commissioned a special report from Greenwich
Associates in order to assess this evolution, as seen by the global buy-side. FX
volume executed electronically increased 55 percent from 2011 to 2012.
Electronically-traded volume was 65 percent of the total, an increase from 57
percent the year before. Multi-dealer platforms account for the majority of volume,
dwarfing the use of single-dealer systems, messaging systems, and dealing on the
telephone. Algorithmic trading already is used by 23 percent of respondents.5
The survey also touched on TCA for FX, with 44 percent of respondents reporting
usage. Early equity TCA was focused on compliance; similarly, 50 percent of users in
FX also report compliance requirements. Surprisingly, 92 percent of respondents
cited investment process improvements as a driver of FX TCA; this type of impetus
was slower to develop in equities. Another 33 percent report client requests for
execution information as being important.
The relative magnitude of fx transaction costs
Before looking at a single order from any individual trader, one goal of TCA is to
quantify transaction costs prevalent in the aggregate market, for various deal sizes,
times of day, and across market conditions. We give a flavor of such analysis for five
major currency pairs and six minors.6 The time period is January 1, 2013 through
March 31, 2013.
The key to the analysis is the construction of a consolidated limit order book for
each currency pair, based on data from twelve banks and five electronic
communications networks (ECNs).7 Tradable quotes are identified, and all statistics
are based upon them; we examine indicative quotes in the next section. We limit
ourselves to a discussion of spot rates.8
The advantage to using an empirical order book is the ability to construct sizeadjusted spreads for any time of day. Intuitively, the spread should depend on the
notional amount available at any given price. The order book quantifies this notion,
based on the cost of climbing the book for any given deal size.
2
See http://investment.russell.com/public/pdfs/Consulting/Asset Class Strategy/0110 RR FX Fees.pdf
3
Michael DuCharme, “First Steps in Foreign Exchange Transaction Cost Analysis,” Journal of Performance Measurement, Spring 2007.
4
Tabb Group, “Just What is Best Execution in FX?” Tabb Group Perspective, July 2008.
5
There also is nascent dark pool activity; see Foreign Exchange Trading Creeps into Dark Pools, Wall Street Journal, October 11. 2012
6
7
8
The majors are EUR.USD, GBP.USD, AUD.USD, USD.CAD, and USD.JPY. The minors are USD.PLN, EUR.PLN, USD.CZK, EUR.CZK, USD.TRY, and
USD.ZAR.
This approach was discussed at length in work by Morgan Stanley, but not acted upon, due to lack of available data. Instead, aggregate
cost models were built based on options pricing formulae and an assumption of Poisson arrivals of orders. See, “A Guide To FX
Transaction Cost Analysis, Parts I and II,” Morgan Stanley White Paper Series, October, 2009 and February, 2010.
Space constraints preclude discussion of forwards and swaps.
3
September 2013
FIGURE 1
Depth of Book for GBP.USD and USD.TRY
TOTAL CUMULATIVE BOOK DEPTH FOR GBP.USD
DEPTH AT BEST LEVEL FOR GBP.USD
300
3.5
Base Currency (MM GBP)
3.0
2.5
2.0
1.5
1.0
0.5
250
200
150
100
50
20:00
22:00
22:00
18:00
20:00
16:00
14:00
12:00
8:00
Time of Day (GMT)
Time of Day (GMT)
TOTAL CUMULATIVE BOOK DEPTH FOR USD.TRY
DEPTH AT BEST LEVEL FOR USD.TRY
3.5
40
3.0
35
Base Currency (MM USD)
2.5
2.0
1.5
1.0
0.5
30
25
20
15
10
5
18:00
16:00
14:00
12:00
10:00
8:00
6:00
0:00
20:00
18:00
16:00
14:00
12:00
10:00
8:00
6:00
4:00
2:00
0:00
22:00
Time of Day (GMT)
Time of Day (GMT)
10th-Percentile
4:00
0
0.0
2:00
Base Currency (MM USD)
10:00
6:00
0:00
22:00
20:00
18:00
16:00
14:00
12:00
8:00
10:00
6:00
4:00
2:00
0:00
4:00
0
0.0
2:00
Base Currency (MM GBP)
4.0
Median
90th-Percentile
Source: ITG
The top panels illustrate depth at the best quote and cumulative depth of book for
GBP.USD, and the bottom panels for USD.TRY. The median number of price levels
from which total depth is calculated is remarkably constant across the day, ranging
between 17 and 20 for GBP.USD and 10 to 12 for USD.TRY.
The basic patterns are the same across all currency pairs. Median depth at the best
quotes is 1mm, rising at most to roughly 1.5mm for the EUR.USD. The 90th
percentile exhibits a bit more fluctuation, but is still relatively constant for GBP.USD
at 3mm; for the EUR.USD pair, the upper percentile range hovers around 5mm.
In comparison, cumulative liquidity across available prices rises to 300mm, on
average, for EUR.USD, and between 200mm and 250mm for the Pound, depending on
time of day. Median cumulative depth for USD.TRY is between 25 and 30 times that
available at the best quotes. This minor pair is not completely representative,
however. Book liquidity is sparse for all CZK pairs and for EUR.PLN. Median
cumulative depth is below 5mm for these pairs. In contrast, USD.PLN exhibits
liquidity on the order of 25mm to 30mm.
Based upon these book data, we construct a measure of cost, by currency pair, time
and order size.9 Figure 2 contains the results of the exercise for two major and two
minor pairs, which are representative of the eleven currency pairs studied.10
9
The measures to follow are based on five-minute intervals and adjusted for daylight saving time regimes. Costs are computed for six
deal sizes: 0.1mm, 2.5mm, 7.5mm, 15mm, 35mm, 75mm, and 200mm; remaining data points are simply interpolated. Median values of
the size-adjusted spreads are illustrated in the graphs.
10
Although extrapolation produces reasonable results for large deal sizes for the CZK pairs and EUR.PLN, the lack of substantial liquidity
on those books precludes reliable estimates past the 5mm deal mark.
4
September 2013
FIGURE 2
Size-Adjusted Spread Distributions by Order Size and Time
COST CURVE, GBP.USD
COST CURVE, USD.CAD
1.6
2.5
1.4
2.0
1.0
Cost (bps)
Cost (bps)
1.2
0.8
0.6
0.4
0
50
100
150
Trade Size (MM)
0.0
200
35
30
Cost (bps)
Cost (bps)
25
20
15
10
5
0
50
0
50
100
150
Trade Size (MM)
200
100
150
Trade Size (MM)
200
COST CURVE, USD.ZAR
COST CURVE, USD.PLN
0
1.0
0.5
0.2
0.0
1.5
100
150
Trade Size (MM)
ALL, 1000GMT
200
18
16
14
12
10
8
6
4
2
0
0
ALL, 1600GMT
50
ALL, 2000GMT
Source: ITG
Time-of-day effects are small during London trading hours; the 1000GMT and
1600GMT curves virtually lie on top of each other. Off-hours trading is substantially
more costly, although such differences are minimized for Asia-Pacific pairs such as
AUD.USD and USD.JPY.
Aggregate costs are far lower than previously reported estimates, such as those cited
for Russell and Record Currency Management. In those cases, time is a factor, since
results date back to 2003. Morgan Stanley reports more current numbers based on
their model-based methodology, which are multiples of those shown here.11 Our
liquidity-based estimates for 50mm GBP, for example, are a tenth of the Morgan
Stanley estimate of 4.35 bps. For USD.CAD, they report 4.40 bps on average, with
minimum cost at 1.82 bps; in contrast, for the same 50mm deal size, the graph
suggests about 0.5 bps. In the case of USD.ZAR, Morgan Stanley’s estimate is seven
times what we see in the aggregate data.
As is often the case, the truth is probably somewhere in between. Our method takes
advantage of real liquidity provision from seventeen sources. The assumption in
deriving size-adjusted spreads is a bit heroic, however. We are assuming a trader’s
ability to sweep the aggregate book, taking advantage of all liquidity for all deal sizes.
While this is closer to reality in equity markets, fragmentation of data sources and
the mixed nature of the dealer-ECN markets in FX suggest that our estimates
constitute a lower bound, at least in view of current trading practice. Finally, we
model size-adjusted spreads only, ignoring latent liquidity and slippage costs. In
contrast, the Morgan Stanley numbers are single-sourced, and depend on models,
11
See, for example, “How Much Does It Cost to Trade 50M?” Morgan Stanley Fixed Income and Trading white paper, June 2013.
5
September 2013
for which waiting times for new orders and volatility are sufficient to determine cost.12
Depth of book is not taken into account. A true reality check awaits serious
examination of individual buy-side data with good time-stamps.
Indicative quotes and their tradable counterparts
Indicative quotes are widely disseminated, and underlie most studies of the FX
market. A comparison of tradable quotes (TQ) to indications (IQ) is therefore of
interest. Although there are some quantitative differences in quote levels across
currencies, patterns are similar enough that the relevant points can be illustrated
using one major and one minor pair. The first comparison appears in Figure 3.
FIGURE 3
Tradable and Indicative Spreads Over Time
IQ- AND TQ-BASED SPREADS, EUR.CZK
5.0
4.5
4.0
3.5
3.0
2.5
2.0
1.5
1.0
0.5
0.0
25
Spread (bps)
20
15
10
5
22:55
20:50
18:45
16:40
14:35
12:30
8:20
Time
Time
Source: ITG
10:25
6:15
4:10
2:05
0:00
22:55
20:50
18:45
16:40
14:35
12:30
8:20
10:25
6:15
4:10
2:05
0
0:00
Spread (bps)
IQ- AND TQ-BASED SPREADS, USD.JPY
Spread_TQ
Spread_IQ
Spreads calculated from indicative quotes are significantly greater than those
computed from tradable quotes. The ratio of the two spreads ranges between five
and ten, on average for all currencies. Although the indicative quotes are not
updated as quickly as tradable quotes, they track each other fairly closely, albeit at
different levels. In other words, the intraday patterns are essentially the same.
Although it is not obvious from the USD.JPY example, the difference between
tradable and indicative quotes narrows during London trading hours for most
currency pairs.
Indicative quotes do not vary by size. A natural question concerns the size of deal
for which the indicative spread “correctly” prices a trade relative to what is actually
available in the market. Figure 4 contains representative plots, taken from the GBP.
USD and USD.ZAR pairs.
12
Earlier work by Morgan Stanley cites EBS as the single data source. The 2013 document from which we make the comparisons simply
gives the source as their own Quantitative Solutions and Innovations group. Their characteristic waiting time can be approximated by
the notional amount of the order, divided by the average size of arriving orders, times the average rate of order flow in the market during
the transaction period. This follows from a statistical distribution assumption for order arrivals.
13
An early example is by Ian Domowitz and Tim Bollerslev, “Trading Patterns and Prices in the Interbank Foreign Exchange Market,” Journal
of Finance 48, 1993.
14
IQ quotes tend to lag the tradable quotes consistently by a few seconds for major currency pairs.
6
September 2013
FIGURE 4
Indicative and Size-Adjusted Spread Costs by Order Size
IQ VS SIZE-ADJUSTED SPREAD COSTS AT 1600 GMT TIME:
GBP.USD, 2013Q1
IQ VS SIZE-ADJUSTED SPREAD COSTS AT 1600 GMT TIME:
USD.ZAR, 2013Q1
1.4
1.2
Cost (bps)
Cost (bps)
1.0
0.8
0.6
0.4
0.2
0.0
0
20 40 60 80 100 120 140 160 180 200
9
8
7
6
5
4
3
2
1
0
0
20 40 60 80 100 120 140 160 180 200
Trade Size (MM)
Source: ITG
Trade Size (MM)
ALL Cost Curve
IQ Half Spread
For the major currency pairs, size-adjusted spreads cross the indicative quote at
deal sizes between 80mm and 120mm. Indicative spreads overstate cost for all
sizes below that range, illustrated here by the GBP.USD pair. For the minors, where
deal sizes tend to be smaller, the crossing point is much lower, at about 50mm in
the example above and for pairs such as EUR.PLN (roughly 30mm).
Linking fx costs to institutional equity demand
One of the grander aspirations of TCA is to provide the “all-in” cost of a transaction.
Appropriate linking of orders is an ongoing issue even in straight equity TCA,
depending on the work flow of any individual buy-side institution. Making the
connection between global equity trades and their corresponding FX costs
eventually will require information from the buy-side, which enables the connection
to be made.
We can provide an idea as to what might be expected. For the first quarter of 2013,
we select the ten most active equity trading firms from our TCA Peer database. All
equity orders requiring a foreign exchange transaction are identified.15 At the end
of each trading day, the size of the FX transaction is calculated based on the
aggregated executed sizes of all equity transactions in a country. We contrast three
polar outcomes. The first is immediate execution of the aggregated FX volume of
all equity trades at the time the last equity order is completed. The second and third
are FX executions at prices which deliver the best and worst outcome of the day,
excluding the period 21:30-22:30 GMT, during which prices are not representative.16
The results of this exercise are contained in Figure 5.
15
For reasons idiosyncratic to our own database, the analysis is restricted to USD pairs, e.g., AUD.USD and USD.TRY.
16
Even excluding this period, the worst prices of the day still are temporally close to this interval.
7
September 2013
FIGURE 5
Range of Spread Costs for Equity-Linked Transactions
EQUITY-LINKED FX TRANSACTION COSTS
40
35
Minimum
Last Trade
Maximum (excl.21:30-22:30GMT)
Spread Costs (bps)
30
25
20
15
10
5
0
EUR.USD
AUD.USD
USD.CAD
USD.JPY
GBP.USD
USD.TRY
USD.PLN
USD.ZAR
USD.CZK
Currency Pairs
Source: ITG
FX order sizes tend to be relatively small, on average, but have some sizable outliers
for certain days. As expected, the magnitude of the ITG Peer client order sizes vary
greatly across currency pairs. Euro and Pound lead the pack with the largest order
sizes of 550mm and 600mm, respectively. The average order sizes at 100mm and
170mm are also substantial for both pairs. Further relating the results to costs in
Figure 2, orders in Canadian currency have order sizes up to 200mm (with an average
around 60mm) and for the Polish Zloty, only about 50mm (9.5mm). The last is not
representative of order sizes in all the minors, however. Equity-linked FX order size
for the South African Rand is in the range of 30mm on average with a maximum
around 90mm.
How much does it cost the average firm to implement the FX leg of an equity
transaction? The answer from this sample is an annualized $13.8 million.
How much could it have cost, if executions were consistently at poor prices? The
annualized figure per firm would be $40.8 million. Enough said.
Buy-Side Data and the Way Forward
The purpose of this article is to outline available evidence with respect to aggregate
FX transaction costs. There are both caveats and opportunities associated with
the exercise.
Our estimates are derived from liquidity information based on seventeen data
sources, all providing tradable quotes, and permitting the construction of an order
book. It is no surprise that indicative quotes are generally useless in judging levels of
cost, for which we provide evidence. In effect, however, we present a lower bound on
transaction costs, with results being a fraction of those presented in other published
sources. The reason for this is an assumption that a trader can sweep the book in a
market fragmented not only by time and space, but also by the proliferation of
dealers and ECNs. The difference between our aggregate estimates and realized cost
represents the opportunity to save money. Hence the rationale for FX TCA, and some
motivation for changes in market structure and sell-side applications, which would
permit such liquidity aggregation.
September 2013
8
We note that a reality check awaits a serious look at a cross-section of buy-side
firms’ FX dealings, using data with good time-stamps. A preliminary examination of
buy-side trading in our own files suggests that process improvement can lower
costs. Trading in EUR.USD, for example, appears to cost roughly three times what
we would have predicted based on the order book. For the AUD.USD pair, the factor
is four. For the ten firms for which we match equity transactions with their FX
counterparts, the cost is $3.5 million, per firm per quarter, based on our lower
bound estimates of size-adjusted spreads. Poor execution multiplies this figure
three-fold. These are serious numbers, which call for a serious attempt at
measurement and analysis.
There is much more to do, and many more questions than answers at this stage.
Forwards and swaps constitute part of our individual buy-side analyses, and similar
aggregate information would be useful, especially for minor currencies. The impact
of the common practice of netting currencies is certainly a topic, especially since
any residuals from that process are executed by a single dealer, as opposed to being
exposed to the type of liquidity described here. The effects of volatility are not yet
well understood. In preliminary work, we find that volatility, per se, may not be as
strong an effect as commonly believed. Volatility surprises, deviations from
expectations, constitute a powerful driver and can be quantified, not only for
forensic analysis, but also as a pre-trade tool. Explicit links between pre-trade
and post-trade analysis have been shown to reduce costs in equity markets.
We believe the same to be true in FX.
How big is “Big?” Regardless of disparities in alternative estimates, FX trading
costs, if not measured and managed correctly, can be a meaningful drag on
investment performance. Solutions now exist, which permit leverage to achieve
better performance. Investors and traders are beginning to expect counterparty
accountability in terms of execution. Focus and measurement are the first
necessary steps.
© 2013 Investment Technology Group, Inc. All rights reserved. Not to be reproduced or retransmitted without permission. 91613-17020
These materials are for informational purposes only, and are not intended to be used for trading or investment purposes or as an offer to sell
or the solicitation of an offer to buy any security or financial product. The information contained herein has been taken from trade and
statistical services and other sources we deem reliable but we do not represent that such information is accurate or complete and it should
not be relied upon as such. No guarantee or warranty is made as to the reasonableness of the assumptions or the accuracy of the models or
market data used by ITG or the actual results that may be achieved. These materials do not provide any form of advice (investment, tax or
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individual author(s) and are not necessarily those of ITG.